{"id":24791,"date":"2024-01-17T10:01:25","date_gmt":"2024-01-17T02:01:25","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=24791"},"modified":"2024-01-17T10:01:28","modified_gmt":"2024-01-17T02:01:28","slug":"%e4%b8%80%e5%88%86%e9%92%9f%e6%95%99%e4%bd%a0%e5%a6%82%e4%bd%95%e8%bf%90%e7%94%a8r%e5%8c%85mass%ef%bc%8c%e4%b8%80%e4%b8%aa%e5%bc%ba%e5%a4%a7%e7%9a%84%e6%89%a9%e5%b1%95%e5%8c%85%e5%b8%a6%e4%bd%a0","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=24791","title":{"rendered":"\u4e00\u5206\u949f\u6559\u4f60\u5982\u4f55\u8fd0\u7528R\u5305MASS\uff0c\u4e00\u4e2a\u5f3a\u5927\u7684\u6269\u5c55\u5305\u5e26\u4f60\u5feb\u901f\u5206\u6790\u6570\u636e\u548c\u5efa\u6a21\u3002"},"content":{"rendered":"

R\u5305MASS\u662fR\u8bed\u8a00\u4e2d\u7684\u4e00\u4e2a\u6269\u5c55\u5305\uff0c\u5168\u79f0\u4e3a”Modern Applied Statistics with S”\u3002\u8be5\u5305\u63d0\u4f9b\u4e86\u8bb8\u591a\u7528\u4e8e\u73b0\u4ee3\u5e94\u7528\u7edf\u8ba1\u5b66\u7684\u51fd\u6570\u548c\u6570\u636e\u96c6\uff0c\u662fR\u8bed\u8a00\u4e2d\u6700\u53d7\u6b22\u8fce\u7684\u6269\u5c55\u5305\u4e4b\u4e00\u3002<\/p>\n

MASS\u5305\u5305\u542b\u4e86\u591a\u79cd\u7528\u4e8e\u7edf\u8ba1\u5efa\u6a21\u3001\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u7684\u51fd\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9bMASS\u5305\u7684\u4e3b\u8981\u529f\u80fd\u548c\u7279\u70b9\uff1a<\/p>\n

1. \u7ebf\u6027\u6a21\u578b\uff1aMASS\u5305\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u7528\u4e8e\u62df\u5408\u7ebf\u6027\u6a21\u578b\u7684\u51fd\u6570\uff0c\u5305\u62eclm()\u51fd\u6570\uff0c\u53ef\u4ee5\u7528\u4e8e\u62df\u5408\u7b80\u5355\u548c\u591a\u91cd\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u3002\u6b64\u5916\uff0c\u5305\u4e2d\u8fd8\u5305\u542b\u4e86\u4e00\u4e9b\u7528\u4e8e\u6a21\u578b\u8bca\u65ad\u548c\u9009\u62e9\u7684\u51fd\u6570\u3002<\/p>\n

2. \u5e7f\u4e49\u7ebf\u6027\u6a21\u578b\uff1aMASS\u5305\u652f\u6301\u5e7f\u4e49\u7ebf\u6027\u6a21\u578b\uff08Generalized Linear Models\uff0cGLMs\uff09\uff0c\u53ef\u4ee5\u62df\u5408\u5305\u62ec\u4e8c\u9879\u5f0f\u56de\u5f52\u3001\u6cca\u677e\u56de\u5f52\u3001\u8d1f\u4e8c\u9879\u5f0f\u56de\u5f52\u7b49\u5728\u5185\u7684\u591a\u79cd\u6a21\u578b\u3002<\/p>\n

3. \u5e7f\u4e49\u53ef\u52a0\u6a21\u578b\uff1aMASS\u5305\u63d0\u4f9b\u4e86\u7528\u4e8e\u62df\u5408\u5e7f\u4e49\u53ef\u52a0\u6a21\u578b\uff08Generalized Additive Models\uff0cGAMs\uff09\u7684\u51fd\u6570\u3002GAMs\u662f\u4e00\u79cd\u975e\u7ebf\u6027\u5e7f\u4e49\u7ebf\u6027\u6a21\u578b\uff0c\u5141\u8bb8\u901a\u8fc7\u6dfb\u52a0\u5e73\u6ed1\u51fd\u6570\u6765\u5efa\u6a21\u9884\u6d4b\u53d8\u91cf\u4e0e\u54cd\u5e94\u53d8\u91cf\u4e4b\u95f4\u7684\u975e\u7ebf\u6027\u5173\u7cfb\u3002<\/p>\n

4. \u4e3b\u6210\u5206\u5206\u6790\uff1aMASS\u5305\u4e2d\u7684prcomp()\u548cprincipal()\u51fd\u6570\u53ef\u4ee5\u7528\u4e8e\u8fdb\u884c\u4e3b\u6210\u5206\u5206\u6790\uff08Principal Component Analysis\uff0cPCA\uff09\uff0c\u7528\u4e8e\u964d\u7ef4\u548c\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n

5. \u805a\u7c7b\u5206\u6790\uff1aMASS\u5305\u5305\u542b\u4e86\u4e00\u4e9b\u7528\u4e8e\u805a\u7c7b\u5206\u6790\u7684\u51fd\u6570\uff0c\u5982kmeans()\u51fd\u6570\u548chclust()\u51fd\u6570\uff0c\u53ef\u4ee5\u7528\u4e8eK\u5747\u503c\u805a\u7c7b\u548c\u5c42\u6b21\u805a\u7c7b\u5206\u6790\u3002<\/p>\n

6. \u6b63\u5219\u5316\uff1aMASS\u5305\u63d0\u4f9b\u4e86\u4e00\u4e9b\u7528\u4e8e\u6b63\u5219\u5316\u7684\u51fd\u6570\uff0c\u5982ridge()\u51fd\u6570\u548classo()\u51fd\u6570\u3002\u8fd9\u4e9b\u51fd\u6570\u53ef\u4ee5\u7528\u4e8e\u62df\u5408\u6b63\u5219\u5316\u7684\u7ebf\u6027\u6a21\u578b\uff0c\u4ee5\u51cf\u5c0f\u8fc7\u62df\u5408\u548c\u6539\u5584\u6a21\u578b\u7684\u9884\u6d4b\u6027\u80fd\u3002<\/p>\n

\u8981\u4f7f\u7528MASS\u5305\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u5c06\u5176\u5b89\u88c5\u548c\u52a0\u8f7d\u5230R\u73af\u5883\u4e2d\uff1a<\/p>\n

install.packages(“MASS”) # \u5b89\u88c5MASS\u5305<\/p>\n

library(MASS) # \u52a0\u8f7dMASS\u5305<\/p>\n

\u793a\u4f8b\uff1a<\/p>\n

    \n
  1. \u7ebf\u6027\u56de\u5f52<\/li>\n<\/ol>\n

    > library(MASS)<\/p>\n

    > lm_model <- lm(medv ~ ., data = Boston)<\/p>\n

    > summary(lm_model)<\/p>\n

    \"Screenshot<\/p>\n

      \n
    1. \u4e3b\u6210\u5206\u5206\u6790<\/li>\n<\/ol>\n

      > library(MASS)<\/p>\n

      > pca_result <- prcomp(USArrests, scale = TRUE)<\/p>\n

      > summary(pca_result)<\/p>\n

      \"Screenshot<\/p>\n

        \n
      1. K\u5747\u503c\u805a\u7c7b<\/li>\n<\/ol>\n

        > library(MASS)<\/p>\n

        > kmeans_result <- kmeans(USArrests, centers = 3)<\/p>\n

        > kmeans_result$cluster<\/p>\n

        \"Screenshot<\/p>\n

          \n
        1. \u5e7f\u4e49\u7ebf\u6027\u6a21\u578b<\/li>\n<\/ol>\n

          > library(MASS)<\/p>\n

          > data(quine)<\/p>\n

          > quine$AbsentBinary <- ifelse(quine$Days > 10, 1, 0)<\/p>\n

          > glm_model <- glm(cbind(AbsentBinary, 10 – AbsentBinary) ~ ., data = quine, family = binomial)<\/p>\n

          > summary(glm_model)<\/p>\n

          \"Screenshot<\/p>\n

          \u4ee5\u4e0a\u5c31\u662f\u5bf9R\u5305MASS\u7684\u7b80\u5355\u4ecb\u7ecd\u5566\uff0c\u5b83\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u7edf\u8ba1\u5b66\u6269\u5c55\u5305\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u5efa\u6a21\u7684\u51fd\u6570\u548c\u6570\u636e\u96c6\u3002\u65e0\u8bba\u662f\u8fdb\u884c\u7ebf\u6027\u56de\u5f52\u3001\u5e7f\u4e49\u7ebf\u6027\u6a21\u578b\u3001\u4e3b\u6210\u5206\u5206\u6790\u8fd8\u662f\u805a\u7c7b\u5206\u6790\uff0cMASS\u5305\u90fd\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5de5\u5177\u548c\u51fd\u6570\u6765\u652f\u6301\u7528\u6237\u7684\u7edf\u8ba1\u5206\u6790\u5de5\u4f5c\u3002<\/p>\n

          \u5c0f\u4f19\u4f34\u4eec\uff0c\u4eca\u5929\u6709\u6ca1\u6709\u5b66\u5230\u65b0\u77e5\u8bc6\u5462\uff0c\u60f3\u8981\u7ee7\u7eed\u4e86\u89e3R\u8bed\u8a00\u5185\u5bb9\u53ef\u4ee5\u6301\u7eed\u5173\u6ce8\u5c0f\u679c\u54e6\uff5e\uff5e\u6216\u8005\u4e5f\u53ef\u4ee5\u5173\u6ce8\u6211\u4eec\u7684\u5b98\u7f51\u4e5f\u4f1a\u6301\u7eed\u66f4\u65b0\u7684\u54e6\uff5e http:\/\/www.biocloudservice.com\/home.html<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

          R\u5305MASS\u662fR\u8bed\u8a00\u4e2d\u7684\u4e00\u4e2a\u6269\u5c55\u5305\uff0c\u5168\u79f0\u4e3a”Modern Applied Statistics w […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":""},"categories":[1],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/24791"}],"collection":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=24791"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/24791\/revisions"}],"predecessor-version":[{"id":24796,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/24791\/revisions\/24796"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=24791"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=24791"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=24791"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}